Knowledge of the shape of the dose-response curve must extend to levels at which humans are typically[unreadable] exposed if we are to accurately assess the risks of adverse effects on the public health from exposures to[unreadable] environmental chemicals. Health effects data are usually sparse at environmental levels of exposure and[unreadable] computational models are being used to estimate both chemical disposition (i.e., pharmacokinetics) and[unreadable] tissue responses (i.e., pharmacodynamics). While current pharmacokinetic models incorporate physiological[unreadable] and anatomical information to provide accurate estimates of target tissue doses, the pharmacodynamic[unreadable] relationship between a chemical at its target site and the ultimate biological effect is usually described[unreadable] empirically or semi-empirically. Molecular level descriptions of pharmacodynamic mechanisms would[unreadable] provide a better understanding of dose-response curves and would reduce uncertainty in safety and risk[unreadable] assessments. The mission of this Core will be to provide the skills and resources needed to develop[unreadable] computational models of biochemical pathways and to thereby provide insight into the adverse health effects[unreadable] of TCDD and related chemicals. Since development of computational models is an iterative process, with[unreadable] model development and laboratory experiments proceeding hand-in-hand, the work in this Core will be[unreadable] collaborative with the work in the Research Projects that the Core supports. The[unreadable] overall approach to be used for development of computational models is defined by 4 Specific Aims:[unreadable] SA1. Develop initial descriptions of biochemical pathways where the nodes of the pathway and the[unreadable] interactions between nodes are linked to biomedical databases.[unreadable] SA2. Develop a directed graph by curating the pathway description obtained under SA1.[unreadable] SA3. Develop computational models based on the network structures described by directed graphs.[unreadable] SA4. Determine if a stochastic or Boolean model is preferable to an ODE-based model for understanding[unreadable] the dynamic behavior of a particular biochemical network.[unreadable] This Core will also seek to train postdoctoral fellows and other staff from the Research Projects in the use of[unreadable] software for development of pathway maps and for computational modeling of the pathways.